{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/DRL_multiple_stock_trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXaoZs2lh1hi"
   },
   "source": [
    "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
    "\n",
    "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
    "\n",
    "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
    "* Check out medium blog for detailed explanations: \n",
    "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uy5_PTmOh1hj"
   },
   "source": [
    "## Python Package Installation\n",
    "\n",
    "As a first step we check if the additional packages needed are present, if not install them. \n",
    "* Yahoo Finance API\n",
    "* pandas\n",
    "* numpy\n",
    "* matplotlib\n",
    "* stockstats\n",
    "* OpenAI gym\n",
    "* stable-baselines\n",
    "* tensorflow\n",
    "* pyfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oirvDTJ0h1hk",
    "outputId": "badf76e7-1383-4e4e-96a9-35105f789e8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting yfinance\n",
      "  Downloading https://files.pythonhosted.org/packages/7a/e8/b9d7104d3a4bf39924799067592d9e59119fcfc900a425a12e80a3123ec8/yfinance-0.1.55.tar.gz\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/28/0b761b64ecbd63d272ed0e7a6ae6e4402fc37886b59181bfdf274424d693/lxml-4.6.1-cp36-cp36m-manylinux1_x86_64.whl (5.5MB)\n",
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      "Building wheels for collected packages: yfinance\n",
      "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22618 sha256=dcee81b2bef7d2e8dff8ce269a173224b490be3c926470d55b37f3c34e32732c\n",
      "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
      "Successfully built yfinance\n",
      "Installing collected packages: lxml, yfinance\n",
      "  Found existing installation: lxml 4.2.6\n",
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      "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
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      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (1.24.3)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio==0.9.2+75.g4b901f6) (3.0.4)\n",
      "Building wheels for collected packages: pyfolio, empyrical\n",
      "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp36-none-any.whl size=75764 sha256=21728994b0ec256aec902e36b89fe0e226e22e85dea4220f03c516b81a869da7\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-turb0l69/wheels/62/7d/a7/3e462442ba7d63c35414176627c886340521dc3dbc0893ce9f\n",
      "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for empyrical: filename=empyrical-0.5.5-cp36-none-any.whl size=39763 sha256=30027cc5bea02873bed61b5fd651ffcc8640204b7eceec1459be4584d41f04dd\n",
      "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
      "Successfully built pyfolio empyrical\n",
      "Installing collected packages: empyrical, pyfolio\n",
      "Successfully installed empyrical-0.5.5 pyfolio-0.9.2+75.g4b901f6\n"
     ]
    }
   ],
   "source": [
    "import pkg_resources\n",
    "import pip\n",
    "installedPackages = {pkg.key for pkg in pkg_resources.working_set}\n",
    "required = {'yfinance', 'pandas','numpy', 'matplotlib', 'stockstats','stable-baselines','gym','tensorflow','pyfolio'}\n",
    "missing = required - installedPackages\n",
    "if missing:\n",
    "    !pip install yfinance\n",
    "    !pip install pandas\n",
    "    !pip install numpy\n",
    "    !pip install matplotlib\n",
    "    !pip install stockstats\n",
    "    !pip install gym\n",
    "    !pip install stable-baselines[mpi]\n",
    "    !pip install tensorflow==1.15.4\n",
    "    !pip install git+https://github.com/quantopian/pyfolio\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nGv01K8Sh1hn"
   },
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "lPqeTTwoh1hn"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.use('Agg')\n",
    "\n",
    "import yfinance as yf\n",
    "from stockstats import StockDataFrame as Sdf\n",
    "import pyfolio\n",
    "\n",
    "import gym\n",
    "from stable_baselines import PPO2, DDPG, A2C, ACKTR, TD3, TRPO\n",
    "from stable_baselines import DDPG\n",
    "from stable_baselines import A2C\n",
    "from stable_baselines import SAC\n",
    "from stable_baselines.common.vec_env import DummyVecEnv\n",
    "from stable_baselines.common.policies import MlpPolicy\n",
    "from stable_baselines.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise, AdaptiveParamNoiseSpec\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A289rQWMh1hq"
   },
   "source": [
    "# Part 1: Download Data\n",
    "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "X2MNEehAh1hq"
   },
   "outputs": [],
   "source": [
    "# Dow 30 constituents at 2019/01/01\n",
    "dow_30_ticker = ['AAPL','MSFT','JPM','V','RTX','PG','GS','NKE','DIS','AXP',\n",
    "                  'HD','INTC','WMT','IBM','MRK','UNH','KO','CAT','TRV','JNJ',\n",
    "                  'CVX','MCD','VZ','CSCO','XOM','BA','MMM','PFE','WBA','DD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TZxcKiCuh1hv",
    "outputId": "c3111ff5-0e42-47f5-e9fe-3c3c5c7f8343"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
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      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# Download and save the data in a pandas DataFrame:\n",
    "dow_30 = pd.DataFrame()\n",
    "for tic in dow_30_ticker:\n",
    "    data_df = yf.download(tic, start=\"2009-01-01\", end=\"2020-10-23\")\n",
    "    data_df['tic'] = tic\n",
    "    dow_30=dow_30.append(data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CV3HrZHLh1hy",
    "outputId": "4221ab23-b07b-4a1b-f5bb-790ee7ea941e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89191, 7)"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "fYeJzIaVh1h1",
    "outputId": "cab23fd0-6b0a-4689-cc78-75a49130762c"
   },
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <td>AAPL</td>\n",
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       "    <tr>\n",
       "      <th>2009-01-05</th>\n",
       "      <td>3.327500</td>\n",
       "      <td>3.435000</td>\n",
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       "      <td>3.377857</td>\n",
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       "      <td>AAPL</td>\n",
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       "    <tr>\n",
       "      <th>2009-01-06</th>\n",
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       "      <td>1.289310e+09</td>\n",
       "      <td>AAPL</td>\n",
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       "    <tr>\n",
       "      <th>2009-01-07</th>\n",
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       "      <td>3.223572</td>\n",
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       "      <td>3.326786</td>\n",
       "      <td>3.215714</td>\n",
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       "      <td>6.735008e+08</td>\n",
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      "text/plain": [
       "                Open      High       Low  ...  Adj Close        Volume   tic\n",
       "Date                                      ...                               \n",
       "2009-01-02  3.067143  3.251429  3.041429  ...   2.800736  7.460152e+08  AAPL\n",
       "2009-01-05  3.327500  3.435000  3.311071  ...   2.918938  1.181608e+09  AAPL\n",
       "2009-01-06  3.426786  3.470357  3.299643  ...   2.870794  1.289310e+09  AAPL\n",
       "2009-01-07  3.278929  3.303571  3.223572  ...   2.808761  7.530488e+08  AAPL\n",
       "2009-01-08  3.229643  3.326786  3.215714  ...   2.860918  6.735008e+08  AAPL\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "ji8k6gfth1h3"
   },
   "outputs": [],
   "source": [
    "# reset the index, we want to use numbers instead of dates\n",
    "dow_30=dow_30.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "SS2sZ4d_h1h6"
   },
   "outputs": [],
   "source": [
    "# convert the column names to standardized names\n",
    "dow_30.columns = ['datadate','open','high','low','close','adjcp','volume','tic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "QcFe8zF3h1h8"
   },
   "outputs": [],
   "source": [
    "# convert date to string format, easy to filter\n",
    "dow_30['datadate']=dow_30.datadate.apply(lambda x: x.strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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    },
    "id": "f6kMMrDQh1h_",
    "outputId": "b5fca1a7-d250-4c2e-e611-19e61fac6106"
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datadate      open      high  ...     adjcp        volume   tic\n",
       "0  2009-01-02  3.067143  3.251429  ...  2.800736  7.460152e+08  AAPL\n",
       "1  2009-01-05  3.327500  3.435000  ...  2.918938  1.181608e+09  AAPL\n",
       "2  2009-01-06  3.426786  3.470357  ...  2.870794  1.289310e+09  AAPL\n",
       "3  2009-01-07  3.278929  3.303571  ...  2.808761  7.530488e+08  AAPL\n",
       "4  2009-01-08  3.229643  3.326786  ...  2.860918  6.735008e+08  AAPL\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kti5dQvBh1iE",
    "outputId": "84132b12-ef9f-4a96-d11b-8855912cffff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89191, 8)"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uqC6c40Zh1iH"
   },
   "source": [
    "# Part 2: Preprocess Data\n",
    "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sJ-n0ZnPh1iH"
   },
   "source": [
    "## Check missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Le342Hc1h1iI",
    "outputId": "04c21ab8-82cf-420d-f422-95b96880fe6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing data \n",
    "dow_30.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "7j4KFI3Uh1iK"
   },
   "outputs": [],
   "source": [
    "dow_30=dow_30.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ShFsAjbjitOU",
    "outputId": "a256317a-79e3-457c-8589-0f7b06596141"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GS      2973\n",
       "CVX     2973\n",
       "WBA     2973\n",
       "INTC    2973\n",
       "PFE     2973\n",
       "HD      2973\n",
       "CAT     2973\n",
       "DIS     2973\n",
       "RTX     2973\n",
       "IBM     2973\n",
       "DD      2973\n",
       "NKE     2973\n",
       "MCD     2973\n",
       "CSCO    2973\n",
       "PG      2973\n",
       "JPM     2973\n",
       "AAPL    2973\n",
       "VZ      2973\n",
       "MSFT    2973\n",
       "MRK     2973\n",
       "XOM     2973\n",
       "AXP     2973\n",
       "BA      2973\n",
       "V       2973\n",
       "MMM     2973\n",
       "JNJ     2973\n",
       "UNH     2973\n",
       "TRV     2973\n",
       "WMT     2973\n",
       "KO      2973\n",
       "Name: tic, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.tic.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WAjH-SPOh1iN",
    "outputId": "962f9589-8e7a-4a4b-9b6c-62666a0fcd0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing data \n",
    "dow_30.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zNm1eCFRh1iP"
   },
   "source": [
    "## Add technical indicators\n",
    "In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "zHKY4sGxh1iQ"
   },
   "outputs": [],
   "source": [
    "def add_technical_indicator(df):\n",
    "        \"\"\"\n",
    "        calcualte technical indicators\n",
    "        use stockstats package to add technical inidactors\n",
    "        :param data: (df) pandas dataframe\n",
    "        :return: (df) pandas dataframe\n",
    "        \"\"\"\n",
    "        stock = Sdf.retype(df.copy())\n",
    "        stock['close'] = stock['adjcp']\n",
    "        unique_ticker = stock.tic.unique()\n",
    "\n",
    "        macd = pd.DataFrame()\n",
    "        rsi = pd.DataFrame()\n",
    "\n",
    "        #temp = stock[stock.tic == unique_ticker[0]]['macd']\n",
    "        for i in range(len(unique_ticker)):\n",
    "            ## macd\n",
    "            temp_macd = stock[stock.tic == unique_ticker[i]]['macd']\n",
    "            temp_macd = pd.DataFrame(temp_macd)\n",
    "            macd = macd.append(temp_macd, ignore_index=True)\n",
    "            ## rsi\n",
    "            temp_rsi = stock[stock.tic == unique_ticker[i]]['rsi_30']\n",
    "            temp_rsi = pd.DataFrame(temp_rsi)\n",
    "            rsi = rsi.append(temp_rsi, ignore_index=True)\n",
    "\n",
    "        df['macd'] = macd\n",
    "        df['rsi'] = rsi\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "WpVbCOz9h1iS",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dow_30 = add_technical_indicator(dow_30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "58lmCFfHh1iU",
    "outputId": "09491ed3-a6a7-4322-ba27-bcc7d6165745"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89190, 10)"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "waMYQ2Xrh1iX",
    "outputId": "fe3d565e-d82d-40f5-9015-799aeabd1c96"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadate</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjcp</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>3.241071</td>\n",
       "      <td>2.800736</td>\n",
       "      <td>7.460152e+08</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-05</td>\n",
       "      <td>3.327500</td>\n",
       "      <td>3.435000</td>\n",
       "      <td>3.311071</td>\n",
       "      <td>3.377857</td>\n",
       "      <td>2.918938</td>\n",
       "      <td>1.181608e+09</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.002652</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-06</td>\n",
       "      <td>3.426786</td>\n",
       "      <td>3.470357</td>\n",
       "      <td>3.299643</td>\n",
       "      <td>3.322143</td>\n",
       "      <td>2.870794</td>\n",
       "      <td>1.289310e+09</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.001886</td>\n",
       "      <td>70.355404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-07</td>\n",
       "      <td>3.278929</td>\n",
       "      <td>3.303571</td>\n",
       "      <td>3.223572</td>\n",
       "      <td>3.250357</td>\n",
       "      <td>2.808761</td>\n",
       "      <td>7.530488e+08</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>-0.000748</td>\n",
       "      <td>50.429229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3.229643</td>\n",
       "      <td>3.326786</td>\n",
       "      <td>3.215714</td>\n",
       "      <td>3.310714</td>\n",
       "      <td>2.860918</td>\n",
       "      <td>6.735008e+08</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>-0.000088</td>\n",
       "      <td>60.227008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datadate      open      high  ...   tic      macd         rsi\n",
       "0  2009-01-02  3.067143  3.251429  ...  AAPL  0.000000         NaN\n",
       "1  2009-01-05  3.327500  3.435000  ...  AAPL  0.002652  100.000000\n",
       "2  2009-01-06  3.426786  3.470357  ...  AAPL  0.001886   70.355404\n",
       "3  2009-01-07  3.278929  3.303571  ...  AAPL -0.000748   50.429229\n",
       "4  2009-01-08  3.229643  3.326786  ...  AAPL -0.000088   60.227008\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "XnSmCG5Kh1iZ",
    "outputId": "6e8ec43d-09c4-4fc1-909b-ca9fc257cdc7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadate</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjcp</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89186</th>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>59.450001</td>\n",
       "      <td>59.939999</td>\n",
       "      <td>58.930000</td>\n",
       "      <td>59.169998</td>\n",
       "      <td>59.169998</td>\n",
       "      <td>2805300.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.578837</td>\n",
       "      <td>54.004849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89187</th>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>59.349998</td>\n",
       "      <td>60.209999</td>\n",
       "      <td>58.209999</td>\n",
       "      <td>58.439999</td>\n",
       "      <td>58.439999</td>\n",
       "      <td>3827900.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.642528</td>\n",
       "      <td>55.825745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89188</th>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>58.680000</td>\n",
       "      <td>60.099998</td>\n",
       "      <td>58.549999</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>3722700.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.619692</td>\n",
       "      <td>54.079485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89189</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>59.160000</td>\n",
       "      <td>60.070000</td>\n",
       "      <td>58.570000</td>\n",
       "      <td>58.660000</td>\n",
       "      <td>58.660000</td>\n",
       "      <td>4201800.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.660152</td>\n",
       "      <td>55.518858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89190</th>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>58.910000</td>\n",
       "      <td>59.619999</td>\n",
       "      <td>58.250000</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>2977100.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         datadate       open       high  ...  tic      macd        rsi\n",
       "89186  2020-10-16  59.450001  59.939999  ...   DD  0.578837  54.004849\n",
       "89187  2020-10-19  59.349998  60.209999  ...   DD  0.642528  55.825745\n",
       "89188  2020-10-20  58.680000  60.099998  ...   DD  0.619692  54.079485\n",
       "89189  2020-10-21  59.160000  60.070000  ...   DD  0.660152  55.518858\n",
       "89190  2020-10-22  58.910000  59.619999  ...   DD       NaN        NaN\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D1w15h8Fh1ib",
    "outputId": "9aa83ba3-9668-4270-e645-b57834142b5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing data \n",
    "dow_30.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tez5y38xh1ie",
    "outputId": "ccf8f34f-82fb-4746-8a75-c6751f98184f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datadate    False\n",
       "open        False\n",
       "high        False\n",
       "low         False\n",
       "close       False\n",
       "adjcp       False\n",
       "volume      False\n",
       "tic         False\n",
       "macd         True\n",
       "rsi          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "Wx0xF6O7h1ig"
   },
   "outputs": [],
   "source": [
    "# fill missing data with forward filling and backward filling\n",
    "dow_30=dow_30.fillna(method='ffill')\n",
    "dow_30=dow_30.fillna(method='bfill')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9uLP0bqzh1ii",
    "outputId": "f7e56709-f31b-49ae-f78b-4d75b60bf615"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing data \n",
    "dow_30.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "4qyebQGAh1ik",
    "outputId": "41878124-17cd-468f-e732-49b1bbf0a695"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadate</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjcp</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89186</th>\n",
       "      <td>2020-10-16</td>\n",
       "      <td>59.450001</td>\n",
       "      <td>59.939999</td>\n",
       "      <td>58.930000</td>\n",
       "      <td>59.169998</td>\n",
       "      <td>59.169998</td>\n",
       "      <td>2805300.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.578837</td>\n",
       "      <td>54.004849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89187</th>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>59.349998</td>\n",
       "      <td>60.209999</td>\n",
       "      <td>58.209999</td>\n",
       "      <td>58.439999</td>\n",
       "      <td>58.439999</td>\n",
       "      <td>3827900.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.642528</td>\n",
       "      <td>55.825745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89188</th>\n",
       "      <td>2020-10-20</td>\n",
       "      <td>58.680000</td>\n",
       "      <td>60.099998</td>\n",
       "      <td>58.549999</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>3722700.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.619692</td>\n",
       "      <td>54.079485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89189</th>\n",
       "      <td>2020-10-21</td>\n",
       "      <td>59.160000</td>\n",
       "      <td>60.070000</td>\n",
       "      <td>58.570000</td>\n",
       "      <td>58.660000</td>\n",
       "      <td>58.660000</td>\n",
       "      <td>4201800.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.660152</td>\n",
       "      <td>55.518858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89190</th>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>58.910000</td>\n",
       "      <td>59.619999</td>\n",
       "      <td>58.250000</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>59.480000</td>\n",
       "      <td>2977100.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>0.660152</td>\n",
       "      <td>55.518858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         datadate       open       high  ...  tic      macd        rsi\n",
       "89186  2020-10-16  59.450001  59.939999  ...   DD  0.578837  54.004849\n",
       "89187  2020-10-19  59.349998  60.209999  ...   DD  0.642528  55.825745\n",
       "89188  2020-10-20  58.680000  60.099998  ...   DD  0.619692  54.079485\n",
       "89189  2020-10-21  59.160000  60.070000  ...   DD  0.660152  55.518858\n",
       "89190  2020-10-22  58.910000  59.619999  ...   DD  0.660152  55.518858\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qL4wcaT-h1im"
   },
   "source": [
    "## Add turbulence index\n",
    "Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level.\n",
    "\n",
    "To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "70-XFED3h1in"
   },
   "outputs": [],
   "source": [
    "def add_turbulence(df):\n",
    "    \"\"\"\n",
    "    add turbulence index from a precalcualted dataframe\n",
    "    :param data: (df) pandas dataframe\n",
    "    :return: (df) pandas dataframe\n",
    "    \"\"\"\n",
    "    turbulence_index = calcualte_turbulence(df)\n",
    "    df = df.merge(turbulence_index, on='datadate')\n",
    "    df = df.sort_values(['datadate','tic']).reset_index(drop=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "def calcualte_turbulence(df):\n",
    "    \"\"\"calculate turbulence index based on dow 30\"\"\"\n",
    "    # can add other market assets\n",
    "    \n",
    "    df_price_pivot=df.pivot(index='datadate', columns='tic', values='adjcp')\n",
    "    unique_date = df.datadate.unique()\n",
    "    # start after a year\n",
    "    start = 252\n",
    "    turbulence_index = [0]*start\n",
    "    #turbulence_index = [0]\n",
    "    count=0\n",
    "    for i in range(start,len(unique_date)):\n",
    "        current_price = df_price_pivot[df_price_pivot.index == unique_date[i]]\n",
    "        hist_price = df_price_pivot[[n in unique_date[0:i] for n in df_price_pivot.index ]]\n",
    "        cov_temp = hist_price.cov()\n",
    "        current_temp=(current_price - np.mean(hist_price,axis=0))\n",
    "        temp = current_temp.values.dot(np.linalg.inv(cov_temp)).dot(current_temp.values.T)\n",
    "        if temp>0:\n",
    "            count+=1\n",
    "            if count>2:\n",
    "                turbulence_temp = temp[0][0]\n",
    "            else:\n",
    "                #avoid large outlier because of the calculation just begins\n",
    "                turbulence_temp=0\n",
    "        else:\n",
    "            turbulence_temp=0\n",
    "        turbulence_index.append(turbulence_temp)\n",
    "    \n",
    "    \n",
    "    turbulence_index = pd.DataFrame({'datadate':df_price_pivot.index,\n",
    "                                     'turbulence':turbulence_index})\n",
    "    return turbulence_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "JsVF4J6wh1ip"
   },
   "outputs": [],
   "source": [
    "## took about 8 mins to finish\n",
    "dow_30 = add_turbulence(dow_30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HJJPCm7kh1ir",
    "outputId": "a4e5583f-074a-42db-bc8d-910c02f516eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89190, 11)"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "Fl82W2rDh1it",
    "outputId": "ffcd8fd6-58f7-4c21-f22c-273f9d3ddf83"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadate</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjcp</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>3.241071</td>\n",
       "      <td>2.800736</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>18.570000</td>\n",
       "      <td>19.520000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>19.330000</td>\n",
       "      <td>15.800624</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.253831</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>42.779999</td>\n",
       "      <td>45.250000</td>\n",
       "      <td>33.680935</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>0.015364</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>46.910000</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.913739</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>16.959999</td>\n",
       "      <td>12.786087</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>0.002537</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datadate       open       high  ...      macd         rsi  turbulence\n",
       "0  2009-01-02   3.067143   3.251429  ...  0.000000  100.000000         0.0\n",
       "1  2009-01-02  18.570000  19.520000  ...  0.000000   52.253831         0.0\n",
       "2  2009-01-02  42.799999  45.560001  ...  0.015364  100.000000         0.0\n",
       "3  2009-01-02  44.910000  46.980000  ...  0.000000   55.913739         0.0\n",
       "4  2009-01-02  16.410000  17.000000  ...  0.002537  100.000000         0.0\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dow_30.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-QsYaY0Dh1iw"
   },
   "source": [
    "# Part 3: Design Environment\n",
    "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a Markov Decision Process (MDP) problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
    "\n",
    "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
    "\n",
    "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "64EoqOrQjiVf"
   },
   "source": [
    "## Environment for Training\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "aUjpqDVxjgmI"
   },
   "outputs": [],
   "source": [
    "## Environment for Training\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from gym.utils import seeding\n",
    "import gym\n",
    "from gym import spaces\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# shares normalization factor\n",
    "# 100 shares per trade\n",
    "HMAX_NORMALIZE = 100\n",
    "# initial amount of money we have in our account\n",
    "INITIAL_ACCOUNT_BALANCE=1000000\n",
    "# total number of stocks in our portfolio\n",
    "STOCK_DIM = 30\n",
    "# transaction fee: 1/1000 reasonable percentage\n",
    "TRANSACTION_FEE_PERCENT = 0.001\n",
    "\n",
    "REWARD_SCALING = 1e-4\n",
    "\n",
    "\n",
    "class StockEnvTrain(gym.Env):\n",
    "    \"\"\"A stock trading environment for OpenAI gym\"\"\"\n",
    "    metadata = {'render.modes': ['human']}\n",
    "\n",
    "    def __init__(self, df,day = 0):\n",
    "        #super(StockEnv, self).__init__()\n",
    "        self.day = day\n",
    "        self.df = df\n",
    "\n",
    "        # action_space normalization and shape is STOCK_DIM\n",
    "        self.action_space = spaces.Box(low = -1, high = 1,shape = (STOCK_DIM,)) \n",
    "        # Shape = 181: [Current Balance]+[prices 1-30]+[owned shares 1-30] \n",
    "        # +[macd 1-30]+ [rsi 1-30] + [cci 1-30] + [adx 1-30]\n",
    "        self.observation_space = spaces.Box(low=0, high=np.inf, shape = (121,))\n",
    "        # load data from a pandas dataframe\n",
    "        self.data = self.df.loc[self.day,:]\n",
    "        self.terminal = False             \n",
    "        # initalize state\n",
    "        self.state = [INITIAL_ACCOUNT_BALANCE] + \\\n",
    "                      self.data.adjcp.values.tolist() + \\\n",
    "                      [0]*STOCK_DIM + \\\n",
    "                      self.data.macd.values.tolist() + \\\n",
    "                      self.data.rsi.values.tolist()\n",
    "                      #self.data.cci.values.tolist() + \\\n",
    "                      #self.data.adx.values.tolist()\n",
    "        # initialize reward\n",
    "        self.reward = 0\n",
    "        self.cost = 0\n",
    "        # memorize all the total balance change\n",
    "        self.asset_memory = [INITIAL_ACCOUNT_BALANCE]\n",
    "        self.rewards_memory = []\n",
    "        self.trades = 0\n",
    "        self._seed()\n",
    "\n",
    "    def _sell_stock(self, index, action):\n",
    "        # perform sell action based on the sign of the action\n",
    "        if self.state[index+STOCK_DIM+1] > 0:\n",
    "            #update balance\n",
    "            self.state[0] += \\\n",
    "            self.state[index+1]*min(abs(action),self.state[index+STOCK_DIM+1]) * \\\n",
    "             (1- TRANSACTION_FEE_PERCENT)\n",
    "\n",
    "            self.state[index+STOCK_DIM+1] -= min(abs(action), self.state[index+STOCK_DIM+1])\n",
    "            self.cost +=self.state[index+1]*min(abs(action),self.state[index+STOCK_DIM+1]) * \\\n",
    "             TRANSACTION_FEE_PERCENT\n",
    "            self.trades+=1\n",
    "        else:\n",
    "            pass\n",
    "    \n",
    "    def _buy_stock(self, index, action):\n",
    "        # perform buy action based on the sign of the action\n",
    "        available_amount = self.state[0] // self.state[index+1]\n",
    "        # print('available_amount:{}'.format(available_amount))\n",
    "\n",
    "        #update balance\n",
    "        self.state[0] -= self.state[index+1]*min(available_amount, action)* \\\n",
    "                          (1+ TRANSACTION_FEE_PERCENT)\n",
    "\n",
    "        self.state[index+STOCK_DIM+1] += min(available_amount, action)\n",
    "\n",
    "        self.cost+=self.state[index+1]*min(available_amount, action)* \\\n",
    "                          TRANSACTION_FEE_PERCENT\n",
    "        self.trades+=1\n",
    "        \n",
    "    def step(self, actions):\n",
    "        # print(self.day)\n",
    "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
    "        # print(actions)\n",
    "\n",
    "        if self.terminal:\n",
    "            plt.plot(self.asset_memory,'r')\n",
    "            plt.savefig('account_value_train.png')\n",
    "            plt.close()\n",
    "            end_total_asset = self.state[0]+ \\\n",
    "            sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):(STOCK_DIM*2+1)]))\n",
    "            print(\"previous_total_asset:{}\".format(self.asset_memory[0]))           \n",
    "\n",
    "            print(\"end_total_asset:{}\".format(end_total_asset))\n",
    "            df_total_value = pd.DataFrame(self.asset_memory)\n",
    "            df_total_value.to_csv('account_value_train.csv')\n",
    "            print(\"total_reward:{}\".format(self.state[0]+sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):61]))- INITIAL_ACCOUNT_BALANCE ))\n",
    "            print(\"total_cost: \", self.cost)\n",
    "            print(\"total_trades: \", self.trades)\n",
    "            df_total_value.columns = ['account_value']\n",
    "            df_total_value['daily_return']=df_total_value.pct_change(1)\n",
    "            sharpe = (252**0.5)*df_total_value['daily_return'].mean()/ \\\n",
    "                  df_total_value['daily_return'].std()\n",
    "            print(\"Sharpe: \",sharpe)\n",
    "            print(\"=================================\")\n",
    "            df_rewards = pd.DataFrame(self.rewards_memory)\n",
    "            df_rewards.to_csv('account_rewards_train.csv')\n",
    "\n",
    "            return self.state, self.reward, self.terminal,{}\n",
    "\n",
    "        else:\n",
    "            actions = actions * HMAX_NORMALIZE\n",
    "            \n",
    "            begin_total_asset = self.state[0]+ \\\n",
    "            sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):61]))\n",
    "            #print(\"begin_total_asset:{}\".format(begin_total_asset))\n",
    "            \n",
    "            argsort_actions = np.argsort(actions)\n",
    "            \n",
    "            sell_index = argsort_actions[:np.where(actions < 0)[0].shape[0]]\n",
    "            buy_index = argsort_actions[::-1][:np.where(actions > 0)[0].shape[0]]\n",
    "\n",
    "            for index in sell_index:\n",
    "                # print('take sell action'.format(actions[index]))\n",
    "                self._sell_stock(index, actions[index])\n",
    "\n",
    "            for index in buy_index:\n",
    "                # print('take buy action: {}'.format(actions[index]))\n",
    "                self._buy_stock(index, actions[index])\n",
    "\n",
    "            self.day += 1\n",
    "            self.data = self.df.loc[self.day,:]         \n",
    "            #load next state\n",
    "            # print(\"stock_shares:{}\".format(self.state[29:]))\n",
    "            self.state =  [self.state[0]] + \\\n",
    "                    self.data.adjcp.values.tolist() + \\\n",
    "                    list(self.state[(STOCK_DIM+1):61]) + \\\n",
    "                    self.data.macd.values.tolist() + \\\n",
    "                    self.data.rsi.values.tolist() \n",
    "            \n",
    "            end_total_asset = self.state[0]+ \\\n",
    "            sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):61]))\n",
    "            \n",
    "            #print(\"end_total_asset:{}\".format(end_total_asset))\n",
    "            \n",
    "            self.reward = end_total_asset - begin_total_asset  \n",
    "            self.rewards_memory.append(self.reward)\n",
    "            \n",
    "            self.reward = self.reward * REWARD_SCALING\n",
    "            # print(\"step_reward:{}\".format(self.reward))\n",
    "            \n",
    "            self.asset_memory.append(end_total_asset)\n",
    "\n",
    "\n",
    "        return self.state, self.reward, self.terminal, {}\n",
    "\n",
    "    def reset(self):\n",
    "        self.asset_memory = [INITIAL_ACCOUNT_BALANCE]\n",
    "        self.day = 0\n",
    "        self.data = self.df.loc[self.day,:]\n",
    "        self.cost = 0\n",
    "        self.trades = 0\n",
    "        self.terminal = False \n",
    "        self.rewards_memory = []\n",
    "        #initiate state\n",
    "        self.state = [INITIAL_ACCOUNT_BALANCE] + \\\n",
    "                      self.data.adjcp.values.tolist() + \\\n",
    "                      [0]*STOCK_DIM + \\\n",
    "                      self.data.macd.values.tolist() + \\\n",
    "                      self.data.rsi.values.tolist() \n",
    "        return self.state\n",
    "    \n",
    "    def render(self, mode='human'):\n",
    "        return self.state\n",
    "\n",
    "    def _seed(self, seed=None):\n",
    "        self.np_random, seed = seeding.np_random(seed)\n",
    "        return [seed]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4QrezXm3jwu4"
   },
   "source": [
    "## Environment for Trading\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "UStwTAVCjuHP"
   },
   "outputs": [],
   "source": [
    "## Environment for Trading\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from gym.utils import seeding\n",
    "import gym\n",
    "from gym import spaces\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# shares normalization factor\n",
    "# 100 shares per trade\n",
    "HMAX_NORMALIZE = 100\n",
    "# initial amount of money we have in our account\n",
    "INITIAL_ACCOUNT_BALANCE=1000000\n",
    "# total number of stocks in our portfolio\n",
    "STOCK_DIM = 30\n",
    "# transaction fee: 1/1000 reasonable percentage\n",
    "TRANSACTION_FEE_PERCENT = 0.001\n",
    "\n",
    "# turbulence index: 90-150 reasonable threshold\n",
    "#TURBULENCE_THRESHOLD = 140\n",
    "REWARD_SCALING = 1e-4\n",
    "\n",
    "class StockEnvTrade(gym.Env):\n",
    "    \"\"\"A stock trading environment for OpenAI gym\"\"\"\n",
    "    metadata = {'render.modes': ['human']}\n",
    "\n",
    "    def __init__(self, df,day = 0,turbulence_threshold=140):\n",
    "        #super(StockEnv, self).__init__()\n",
    "        #money = 10 , scope = 1\n",
    "        self.day = day\n",
    "        self.df = df\n",
    "        # action_space normalization and shape is STOCK_DIM\n",
    "        self.action_space = spaces.Box(low = -1, high = 1,shape = (STOCK_DIM,)) \n",
    "        # Shape = 181: [Current Balance]+[prices 1-30]+[owned shares 1-30] \n",
    "        # +[macd 1-30]+ [rsi 1-30] + [cci 1-30] + [adx 1-30]\n",
    "        self.observation_space = spaces.Box(low=0, high=np.inf, shape = (121,))\n",
    "        # load data from a pandas dataframe\n",
    "        self.data = self.df.loc[self.day,:]\n",
    "        self.terminal = False     \n",
    "        self.turbulence_threshold = turbulence_threshold\n",
    "        # initalize state\n",
    "        self.state = [INITIAL_ACCOUNT_BALANCE] + \\\n",
    "                      self.data.adjcp.values.tolist() + \\\n",
    "                      [0]*STOCK_DIM + \\\n",
    "                      self.data.macd.values.tolist() + \\\n",
    "                      self.data.rsi.values.tolist() \n",
    "\n",
    "        # initialize reward\n",
    "        self.reward = 0\n",
    "        self.turbulence = 0\n",
    "        self.cost = 0\n",
    "        self.trades = 0\n",
    "        # memorize all the total balance change\n",
    "        self.asset_memory = [INITIAL_ACCOUNT_BALANCE]\n",
    "        self.rewards_memory = []\n",
    "        self.actions_memory=[]\n",
    "        self.date_memory=[]\n",
    "        self._seed()\n",
    "\n",
    "\n",
    "    def _sell_stock(self, index, action):\n",
    "        # perform sell action based on the sign of the action\n",
    "        if self.turbulence<self.turbulence_threshold:\n",
    "            if self.state[index+STOCK_DIM+1] > 0:\n",
    "                #update balance\n",
    "                self.state[0] += \\\n",
    "                self.state[index+1]*min(abs(action),self.state[index+STOCK_DIM+1]) * \\\n",
    "                 (1- TRANSACTION_FEE_PERCENT)\n",
    "                \n",
    "                self.state[index+STOCK_DIM+1] -= min(abs(action), self.state[index+STOCK_DIM+1])\n",
    "                self.cost +=self.state[index+1]*min(abs(action),self.state[index+STOCK_DIM+1]) * \\\n",
    "                 TRANSACTION_FEE_PERCENT\n",
    "                self.trades+=1\n",
    "            else:\n",
    "                pass\n",
    "        else:\n",
    "            # if turbulence goes over threshold, just clear out all positions \n",
    "            if self.state[index+STOCK_DIM+1] > 0:\n",
    "                #update balance\n",
    "                self.state[0] += self.state[index+1]*self.state[index+STOCK_DIM+1]* \\\n",
    "                              (1- TRANSACTION_FEE_PERCENT)\n",
    "                self.state[index+STOCK_DIM+1] =0\n",
    "                self.cost += self.state[index+1]*self.state[index+STOCK_DIM+1]* \\\n",
    "                              TRANSACTION_FEE_PERCENT\n",
    "                self.trades+=1\n",
    "            else:\n",
    "                pass\n",
    "    \n",
    "    def _buy_stock(self, index, action):\n",
    "        # perform buy action based on the sign of the action\n",
    "        if self.turbulence< self.turbulence_threshold:\n",
    "            available_amount = self.state[0] // self.state[index+1]\n",
    "            # print('available_amount:{}'.format(available_amount))\n",
    "            \n",
    "            #update balance\n",
    "            self.state[0] -= self.state[index+1]*min(available_amount, action)* \\\n",
    "                              (1+ TRANSACTION_FEE_PERCENT)\n",
    "\n",
    "            self.state[index+STOCK_DIM+1] += min(available_amount, action)\n",
    "            \n",
    "            self.cost+=self.state[index+1]*min(available_amount, action)* \\\n",
    "                              TRANSACTION_FEE_PERCENT\n",
    "            self.trades+=1\n",
    "        else:\n",
    "            # if turbulence goes over threshold, just stop buying\n",
    "            pass\n",
    "        \n",
    "    def step(self, actions):\n",
    "        # print(self.day)\n",
    "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
    "        # print(actions)\n",
    "\n",
    "        if self.terminal:\n",
    "            plt.plot(self.asset_memory,'r')\n",
    "            plt.savefig('account_value_trade.png')\n",
    "            plt.close()\n",
    "            \n",
    "            df_date = pd.DataFrame(self.date_memory)\n",
    "            df_date.columns = ['datadate']\n",
    "            df_date.to_csv('df_date.csv')\n",
    "            \n",
    "            \n",
    "            df_actions = pd.DataFrame(self.actions_memory)\n",
    "            df_actions.columns = self.data.tic.values\n",
    "            df_actions.index = df_date.datadate                               \n",
    "            df_actions.to_csv('df_actions.csv')\n",
    "            \n",
    "            df_total_value = pd.DataFrame(self.asset_memory)\n",
    "            df_total_value.to_csv('account_value_trade.csv')\n",
    "            end_total_asset = self.state[0]+ \\\n",
    "            sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):(STOCK_DIM*2+1)]))\n",
    "            print(\"previous_total_asset:{}\".format(self.asset_memory[0]))           \n",
    "\n",
    "            print(\"end_total_asset:{}\".format(end_total_asset))\n",
    "            print(\"total_reward:{}\".format(self.state[0]+sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):61]))- self.asset_memory[0] ))\n",
    "            print(\"total_cost: \", self.cost)\n",
    "            print(\"total trades: \", self.trades)\n",
    "\n",
    "            df_total_value.columns = ['account_value']\n",
    "            df_total_value['daily_return']=df_total_value.pct_change(1)\n",
    "            sharpe = (252**0.5)*df_total_value['daily_return'].mean()/ \\\n",
    "                  df_total_value['daily_return'].std()\n",
    "            print(\"Sharpe: \",sharpe)\n",
    "            \n",
    "            df_rewards = pd.DataFrame(self.rewards_memory)\n",
    "            df_rewards.to_csv('account_rewards_trade.csv')\n",
    "            \n",
    "            # print('total asset: {}'.format(self.state[0]+ sum(np.array(self.state[1:29])*np.array(self.state[29:]))))\n",
    "            #with open('obs.pkl', 'wb') as f:  \n",
    "            #    pickle.dump(self.state, f)\n",
    "            \n",
    "            return self.state, self.reward, self.terminal,{}\n",
    "\n",
    "        else:\n",
    "            # print(np.array(self.state[1:29]))\n",
    "            self.date_memory.append(self.data.datadate.unique())\n",
    "  \n",
    "            #print(self.data)\n",
    "            actions = actions * HMAX_NORMALIZE\n",
    "            if self.turbulence>=self.turbulence_threshold:\n",
    "                actions=np.array([-HMAX_NORMALIZE]*STOCK_DIM)\n",
    "            self.actions_memory.append(actions)\n",
    "            \n",
    "            #actions = (actions.astype(int))\n",
    "            \n",
    "            begin_total_asset = self.state[0]+ \\\n",
    "            sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):(STOCK_DIM*2+1)]))\n",
    "            #print(\"begin_total_asset:{}\".format(begin_total_asset))\n",
    "            \n",
    "            argsort_actions = np.argsort(actions)\n",
    "            #print(argsort_actions)\n",
    "            \n",
    "            sell_index = argsort_actions[:np.where(actions < 0)[0].shape[0]]\n",
    "            buy_index = argsort_actions[::-1][:np.where(actions > 0)[0].shape[0]]\n",
    "\n",
    "            for index in sell_index:\n",
    "                # print('take sell action'.format(actions[index]))\n",
    "                self._sell_stock(index, actions[index])\n",
    "\n",
    "            for index in buy_index:\n",
    "                # print('take buy action: {}'.format(actions[index]))\n",
    "                self._buy_stock(index, actions[index])\n",
    "\n",
    "            self.day += 1\n",
    "            self.data = self.df.loc[self.day,:]         \n",
    "            self.turbulence = self.data['turbulence'].values[0]\n",
    "            #print(self.turbulence)\n",
    "            #load next state\n",
    "            # print(\"stock_shares:{}\".format(self.state[29:]))\n",
    "            self.state =  [self.state[0]] + \\\n",
    "                    self.data.adjcp.values.tolist() + \\\n",
    "                    list(self.state[(STOCK_DIM+1):(STOCK_DIM*2+1)]) + \\\n",
    "                    self.data.macd.values.tolist() + \\\n",
    "                    self.data.rsi.values.tolist() \n",
    "            \n",
    "            end_total_asset = self.state[0]+ \\\n",
    "            sum(np.array(self.state[1:(STOCK_DIM+1)])*np.array(self.state[(STOCK_DIM+1):(STOCK_DIM*2+1)]))\n",
    "            \n",
    "            #print(\"end_total_asset:{}\".format(end_total_asset))\n",
    "            \n",
    "            self.reward = end_total_asset - begin_total_asset  \n",
    "            self.rewards_memory.append(self.reward)\n",
    "            \n",
    "            self.reward = self.reward * REWARD_SCALING\n",
    "            \n",
    "            self.asset_memory.append(end_total_asset)\n",
    "\n",
    "        return self.state, self.reward, self.terminal, {}\n",
    "\n",
    "    def reset(self):  \n",
    "        self.asset_memory = [INITIAL_ACCOUNT_BALANCE]\n",
    "        self.day = 0\n",
    "        self.data = self.df.loc[self.day,:]\n",
    "        self.turbulence = 0\n",
    "        self.cost = 0\n",
    "        self.trades = 0\n",
    "        self.terminal = False \n",
    "        #self.iteration=self.iteration\n",
    "        self.rewards_memory = []\n",
    "        self.actions_memory=[]\n",
    "        self.date_memory=[]\n",
    "        #initiate state\n",
    "        self.state = [INITIAL_ACCOUNT_BALANCE] + \\\n",
    "                      self.data.adjcp.values.tolist() + \\\n",
    "                      [0]*STOCK_DIM + \\\n",
    "                      self.data.macd.values.tolist() + \\\n",
    "                      self.data.rsi.values.tolist() \n",
    "\n",
    "        return self.state\n",
    "    \n",
    "    def render(self, mode='human',close=False):\n",
    "        return self.state\n",
    "    \n",
    "\n",
    "    def _seed(self, seed=None):\n",
    "        self.np_random, seed = seeding.np_random(seed)\n",
    "        return [seed]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMNR5nHjh1iz"
   },
   "source": [
    "# Part 4: Implement DRL Algorithms\n",
    "The implementation of the DRL algorithms are based on OpenAI Baselines and Stable Baselines. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDmqOyF9h1iz"
   },
   "source": [
    "## Training data split: 2009-01-01 to 2018-12-31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "lN3DB4nFh1iz"
   },
   "outputs": [],
   "source": [
    "def data_split(df,start,end):\n",
    "    \"\"\"\n",
    "    split the dataset into training or testing using date\n",
    "    :param data: (df) pandas dataframe, start, end\n",
    "    :return: (df) pandas dataframe\n",
    "    \"\"\"\n",
    "    data = df[(df.datadate >= start) & (df.datadate < end)]\n",
    "    data=data.sort_values(['datadate','tic'],ignore_index=True)\n",
    "    data.index = data.datadate.factorize()[0]\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "UZaEAu0Bh1i4"
   },
   "outputs": [],
   "source": [
    "## set up train & test environment\n",
    "# training env\n",
    "train = data_split(dow_30, start='2009-01-01', end='2019-01-01')\n",
    "env_train = DummyVecEnv([lambda: StockEnvTrain(train)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "6bCKAexCh1i7",
    "outputId": "d5a8a134-7f3b-48f5-a195-31d973a99053"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datadate</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjcp</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>3.241071</td>\n",
       "      <td>2.800736</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>18.570000</td>\n",
       "      <td>19.520000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>19.330000</td>\n",
       "      <td>15.800624</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.253831</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>42.779999</td>\n",
       "      <td>45.250000</td>\n",
       "      <td>33.680935</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>0.015364</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>46.910000</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.913739</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>16.959999</td>\n",
       "      <td>12.786087</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>0.002537</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datadate       open       high  ...      macd         rsi  turbulence\n",
       "0  2009-01-02   3.067143   3.251429  ...  0.000000  100.000000         0.0\n",
       "0  2009-01-02  18.570000  19.520000  ...  0.000000   52.253831         0.0\n",
       "0  2009-01-02  42.799999  45.560001  ...  0.015364  100.000000         0.0\n",
       "0  2009-01-02  44.910000  46.980000  ...  0.000000   55.913739         0.0\n",
       "0  2009-01-02  16.410000  17.000000  ...  0.002537  100.000000         0.0\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qxuiMdpOh1i-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uijiWgkuh1jB"
   },
   "source": [
    "## Model training: DDPG\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hp6cU5FZh1jC",
    "outputId": "f5f20759-d073-4065-b9ae-dae01b147fb2",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:502: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
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      "end_total_asset:3621173.5631034113\n",
      "total_reward:2621173.5631034113\n",
      "total_cost:  26619.729435718455\n",
      "total_trades:  45724\n",
      "Sharpe:  0.8284715228611951\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:3185928.208313104\n",
      "total_reward:2185928.208313104\n",
      "total_cost:  25793.69676742264\n",
      "total_trades:  45615\n",
      "Sharpe:  0.692197173119648\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4723178.994645145\n",
      "total_reward:3723178.994645145\n",
      "total_cost:  28672.090834107523\n",
      "total_trades:  41284\n",
      "Sharpe:  0.9358352281063774\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:5218594.942205739\n",
      "total_reward:4218594.942205739\n",
      "total_cost:  15254.725468891389\n",
      "total_trades:  46210\n",
      "Sharpe:  1.0334920534496734\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4153318.2886068984\n",
      "total_reward:3153318.2886068984\n",
      "total_cost:  39483.74309298818\n",
      "total_trades:  41020\n",
      "Sharpe:  0.9272977622545675\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:6312383.644194547\n",
      "total_reward:5312383.644194547\n",
      "total_cost:  23536.154325076797\n",
      "total_trades:  45458\n",
      "Sharpe:  1.12355448609739\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:5172603.216083085\n",
      "total_reward:4172603.216083085\n",
      "total_cost:  32221.126696426978\n",
      "total_trades:  44982\n",
      "Sharpe:  1.0985469649082866\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:6314860.433967897\n",
      "total_reward:5314860.433967897\n",
      "total_cost:  31277.82672781919\n",
      "total_trades:  44335\n",
      "Sharpe:  1.1006384488361125\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4073826.1389666325\n",
      "total_reward:3073826.1389666325\n",
      "total_cost:  26916.589264225757\n",
      "total_trades:  39810\n",
      "Sharpe:  0.7981171574628607\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4429507.234063293\n",
      "total_reward:3429507.234063293\n",
      "total_cost:  23838.431451140106\n",
      "total_trades:  42536\n",
      "Sharpe:  1.0322521708583792\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4942957.910182452\n",
      "total_reward:3942957.910182452\n",
      "total_cost:  22468.681249836645\n",
      "total_trades:  40547\n",
      "Sharpe:  1.0729033163855999\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:5304047.330011157\n",
      "total_reward:4304047.330011157\n",
      "total_cost:  19471.312314340877\n",
      "total_trades:  38765\n",
      "Sharpe:  1.1231797040446765\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:5486830.588116016\n",
      "total_reward:4486830.588116016\n",
      "total_cost:  20590.744792730064\n",
      "total_trades:  40969\n",
      "Sharpe:  1.0046014033165942\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4003954.487566444\n",
      "total_reward:3003954.487566444\n",
      "total_cost:  19041.223158526718\n",
      "total_trades:  37756\n",
      "Sharpe:  1.0675761904230565\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:5508504.875264914\n",
      "total_reward:4508504.875264914\n",
      "total_cost:  27526.008429083642\n",
      "total_trades:  46301\n",
      "Sharpe:  1.0852139696022454\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4441618.765582957\n",
      "total_reward:3441618.7655829573\n",
      "total_cost:  23883.015309109916\n",
      "total_trades:  43637\n",
      "Sharpe:  0.8372733662560499\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:3997803.6345093376\n",
      "total_reward:2997803.6345093376\n",
      "total_cost:  36258.80583239373\n",
      "total_trades:  42686\n",
      "Sharpe:  0.8974759525307473\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:6397494.782383307\n",
      "total_reward:5397494.782383307\n",
      "total_cost:  30554.203612091587\n",
      "total_trades:  45118\n",
      "Sharpe:  1.163148270803249\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4632805.5022079665\n",
      "total_reward:3632805.5022079665\n",
      "total_cost:  19371.73139547927\n",
      "total_trades:  46359\n",
      "Sharpe:  0.9643332737611163\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:5231110.644676718\n",
      "total_reward:4231110.644676718\n",
      "total_cost:  29364.396292053498\n",
      "total_trades:  44614\n",
      "Sharpe:  1.1160197868097699\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:3737244.1886901204\n",
      "total_reward:2737244.1886901204\n",
      "total_cost:  15226.67444451497\n",
      "total_trades:  40083\n",
      "Sharpe:  1.0117768122784647\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:6174522.656463514\n",
      "total_reward:5174522.656463514\n",
      "total_cost:  29819.03853018055\n",
      "total_trades:  43023\n",
      "Sharpe:  1.197716341316776\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4068805.3436167724\n",
      "total_reward:3068805.3436167724\n",
      "total_cost:  13610.584215276807\n",
      "total_trades:  40257\n",
      "Sharpe:  0.945602960622788\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:3709390.3295467054\n",
      "total_reward:2709390.3295467054\n",
      "total_cost:  11386.441131635194\n",
      "total_trades:  46939\n",
      "Sharpe:  0.9121059477293767\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:3359865.2091283626\n",
      "total_reward:2359865.2091283626\n",
      "total_cost:  15305.588189369704\n",
      "total_trades:  41036\n",
      "Sharpe:  0.7158804163629157\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4303177.44944591\n",
      "total_reward:3303177.44944591\n",
      "total_cost:  21387.21412810387\n",
      "total_trades:  47714\n",
      "Sharpe:  0.9326424633329661\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4027840.100811412\n",
      "total_reward:3027840.100811412\n",
      "total_cost:  23522.289382912782\n",
      "total_trades:  44564\n",
      "Sharpe:  0.9304089900428055\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4148789.6671692617\n",
      "total_reward:3148789.6671692617\n",
      "total_cost:  20215.474092671975\n",
      "total_trades:  39329\n",
      "Sharpe:  0.9430796285901052\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4512130.501324894\n",
      "total_reward:3512130.501324894\n",
      "total_cost:  21611.529015011114\n",
      "total_trades:  44061\n",
      "Sharpe:  0.9971954367778807\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4305970.03810141\n",
      "total_reward:3305970.0381014096\n",
      "total_cost:  27769.664910763408\n",
      "total_trades:  40254\n",
      "Sharpe:  0.9227767261005677\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4130002.828180163\n",
      "total_reward:3130002.828180163\n",
      "total_cost:  24295.69434663266\n",
      "total_trades:  44081\n",
      "Sharpe:  0.9646038831439377\n",
      "=================================\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:4695584.749942718\n",
      "total_reward:3695584.749942718\n",
      "total_cost:  27900.79133090684\n",
      "total_trades:  50547\n",
      "Sharpe:  0.9660694576567045\n",
      "=================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<stable_baselines.ddpg.ddpg.DDPG at 0x7fa3671afd68>"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## tensorboard --logdir ./multiple_stock_tensorboard/\n",
    "# add noise to the action in DDPG helps in learning for better exploration\n",
    "n_actions = env_train.action_space.shape[-1]\n",
    "param_noise = None\n",
    "action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions))\n",
    "\n",
    "# model settings\n",
    "model_ddpg = DDPG('MlpPolicy', \n",
    "                   env_train,\n",
    "                   batch_size=64,\n",
    "                   buffer_size=100000,\n",
    "                   param_noise=param_noise, \n",
    "                   action_noise=action_noise,\n",
    "                   verbose=0, \n",
    "                   tensorboard_log=\"./multiple_stock_tensorboard/\")\n",
    "\n",
    "## 250k timesteps: took about 20 mins to finish\n",
    "model_ddpg.learn(total_timesteps=250000, tb_log_name=\"DDPG_run_1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f2wZgkQXh1jE"
   },
   "source": [
    "## Trading\n",
    "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bEv5KGC8h1jE"
   },
   "source": [
    "### Set turbulence threshold\n",
    "Set the turbulence threshold to be the 99% quantile of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "efwBi84ch1jE"
   },
   "outputs": [],
   "source": [
    "insample_turbulence = dow_30[(dow_30.datadate<'2019-01-01') & (dow_30.datadate>='2009-01-01')]\n",
    "insample_turbulence = insample_turbulence.drop_duplicates(subset=['datadate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VHZMBpSqh1jG",
    "outputId": "422914c0-00f1-4ed1-c01f-08b0168715be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2516.000000\n",
       "mean       76.015337\n",
       "std        38.164470\n",
       "min         0.000000\n",
       "25%        58.170414\n",
       "50%        73.987104\n",
       "75%        97.036673\n",
       "max       230.745459\n",
       "Name: turbulence, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_turbulence.turbulence.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "yuwDPkV9h1jL"
   },
   "outputs": [],
   "source": [
    "insample_turbulence_threshold = np.quantile(insample_turbulence.turbulence.values, 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wwoz_7VSh1jO",
    "outputId": "1123da04-01e0-40eb-c419-79013942d59d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177.39111807427827"
      ]
     },
     "execution_count": 62,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_turbulence_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5mmgQF_h1jQ"
   },
   "source": [
    "### Prepare test data and environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "eLOnL5eYh1jR"
   },
   "outputs": [],
   "source": [
    "# test data\n",
    "test = data_split(dow_30, start='2019-01-01', end='2020-10-30')\n",
    "# testing env\n",
    "env_test = DummyVecEnv([lambda: StockEnvTrade(test, turbulence_threshold=insample_turbulence_threshold)])\n",
    "obs_test = env_test.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8i1AF6DMh1jU"
   },
   "source": [
    "### Prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "aRwQ84Zch1jU"
   },
   "outputs": [],
   "source": [
    "def DRL_prediction(model, data, env, obs):\n",
    "    print(\"==============Model Prediction===========\")\n",
    "    for i in range(len(data.index.unique())):\n",
    "        action, _states = model.predict(obs)\n",
    "        obs, rewards, dones, info = env.step(action)\n",
    "        env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2swWs0ybh1jX",
    "outputId": "6d4c7107-ce7d-4be9-8b85-92ee579a3e13"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Model Prediction===========\n",
      "previous_total_asset:1000000\n",
      "end_total_asset:1355928.5577614377\n",
      "total_reward:355928.5577614377\n",
      "total_cost:  25209.989530353247\n",
      "total trades:  6428\n",
      "Sharpe:  1.1462130668402424\n"
     ]
    }
   ],
   "source": [
    "DRL_prediction(model=model_ddpg, \n",
    "               data=test,\n",
    "               env=env_test, \n",
    "               obs=obs_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W6vvNSC6h1jZ"
   },
   "source": [
    "# Part 5: Backtest Our Strategy\n",
    "For simplicity purposes, in the article, we just calculate the Sharpe ratio and the annual return manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "poLh4vV4h1jZ"
   },
   "outputs": [],
   "source": [
    "def backtest_strat(df):\n",
    "    strategy_ret= df.copy()\n",
    "    strategy_ret['Date'] = pd.to_datetime(strategy_ret['Date'])\n",
    "    strategy_ret.set_index('Date', drop = False, inplace = True)\n",
    "    strategy_ret.index = strategy_ret.index.tz_localize('UTC')\n",
    "    del strategy_ret['Date']\n",
    "    ts = pd.Series(strategy_ret['daily_return'].values, index=strategy_ret.index)\n",
    "    return ts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9U6Suru3h1jc"
   },
   "source": [
    "## Dow Jones Industrial Average\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YktexHcqh1jc",
    "outputId": "d5e50f54-3ba9-496a-851b-372c93516129"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "dji = yf.download(\"^DJI\", start=\"2019-01-01\", end=\"2020-10-30\")\n",
    "dji=dji.reset_index()\n",
    "dji.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'adjcp', 'Volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "t9y9KO42h1je"
   },
   "outputs": [],
   "source": [
    "def get_buy_and_hold_sharpe(test):\n",
    "    test['daily_return']=test['adjcp'].pct_change(1)\n",
    "    sharpe = (252**0.5)*test['daily_return'].mean()/ \\\n",
    "    test['daily_return'].std()\n",
    "    annual_return = ((test['daily_return'].mean()+1)**252-1)*100\n",
    "    print(\"annual return: \", annual_return)\n",
    "\n",
    "    print(\"sharpe ratio: \", sharpe)\n",
    "    #return sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QVj2d0KCh1jh",
    "outputId": "cc6ece78-40a5-4cce-f3a8-85b83b605d8c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annual return:  12.28712950381945\n",
      "sharpe ratio:  0.4088681653702324\n"
     ]
    }
   ],
   "source": [
    "## get manual results\n",
    "get_buy_and_hold_sharpe(dji)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "VVS68MGqh1jk"
   },
   "outputs": [],
   "source": [
    "dji['daily_return']=dji['adjcp'].pct_change(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "MW4fSXm_h1jn"
   },
   "outputs": [],
   "source": [
    "# input for pyfolio\n",
    "dow_strat = backtest_strat(dji)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mg3F7457h1jr"
   },
   "source": [
    "## Our DRL trading strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "zp0f60nrh1js"
   },
   "outputs": [],
   "source": [
    "def get_daily_return(df):\n",
    "    df['daily_return']=df.account_value.pct_change(1)\n",
    "    #df=df.dropna()\n",
    "    sharpe = (252**0.5)*df['daily_return'].mean()/ \\\n",
    "    df['daily_return'].std()\n",
    "    \n",
    "    annual_return = ((df['daily_return'].mean()+1)**252-1)*100\n",
    "    print(\"annual return: \", annual_return)\n",
    "    print(\"sharpe ratio: \", sharpe)    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "UnU9qA0Wh1ju"
   },
   "outputs": [],
   "source": [
    "## load results\n",
    "df_account_value = pd.read_csv('account_value_trade.csv',index_col=0)\n",
    "df_account_value = pd.DataFrame({'account_value':df_account_value['0']})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ub1ONkJ9h1jy",
    "outputId": "5d9b1de7-cfc2-43fd-98e8-1a92b0a4dfc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annual return:  19.81035775555171\n",
      "sharpe ratio:  1.1462130668402417\n"
     ]
    }
   ],
   "source": [
    "## get manual results\n",
    "df_account_value=get_daily_return(df_account_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "90mCnh-fh1j1"
   },
   "outputs": [],
   "source": [
    "# input for pyfolio\n",
    "df_account_value['Date'] = dji['Date']\n",
    "DRL_strat = backtest_strat(df_account_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zdyVEkLKuDd-"
   },
   "source": [
    "## Plot the results using Quantopian pyfolio\n",
    "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9CIAlgQZh1j4",
    "outputId": "09d67096-761a-4e4f-b4a4-060cf66a5e0e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-02</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-10-22</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>21</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>18.282%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>35.593%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>15.774%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-12.201%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-1.916%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.20</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.61</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>2020-09-23</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.63</td>\n",
       "      <td>2019-07-29</td>\n",
       "      <td>2019-08-05</td>\n",
       "      <td>2019-08-29</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.79</td>\n",
       "      <td>2019-09-12</td>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>2019-10-11</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.22</td>\n",
       "      <td>2019-11-18</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
      "  'interesting times.', UserWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x5184 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "with pyfolio.plotting.plotting_context(font_scale=1.1):\n",
    "    pyfolio.create_full_tear_sheet(returns = DRL_strat,\n",
    "                                   benchmark_rets=dow_strat, set_context=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "A6W2J57ch1j9"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "DRL_multiple_stock_trading.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
